Tri-Institutional Training Program in Computational Biology and Medicine

描述(由申请人提供)：计算生物学和医学(CBM)三所院校培训计划利用纽约州伊萨卡市的康奈尔大学、纽约市的威尔·康奈尔医学院和斯隆-凯特琳研究所(斯隆-凯特琳癌症纪念中心的研究机构)优秀的教育和研究资源，培训计算生物学家，使他们掌握解决生物学和医学所特有的复杂问题所需的跨学科方法。CBM培训环境旨在应对在这一领域培训科学家的独特挑战，其特点是：定量和生物科学的课程工作；研究轮换，以便能够进行知情的论文选题；受训人员正在进行的研讨会系列，以加强计划的凝聚力和培养相关学科的流利性；在各种基础到翻译的计算和实验实验室之一进行论文研究；定量和生物指导，以确保培训的平衡和广度；鼓励参与混合计算/实验项目，以促进理论和实验之间的联系。课程所有组成部分的基本主题是学员接触计算生物学的不同方法和领域，这反映了这样一个事实，即跨学科方法对于解决当今生物学和医学中的许多问题是宝贵的。该计划成立已有10年，培训成功的记录也在不断扩大。通过这项竞争性续签建议，我们要求将T32的名额从6个增加到12个，我们认为这是合理的，因为有大量符合培训资助资格的申请者，以及具有领先教师的大量尖端论文研究机会 科学家，以及不断增加的临界学生群体所固有的培训内容。 申请的T32资金将极大地帮助CBM计划继续实现其使命，即培训下一代科学家使用计算和分析方法来解决生物学中的复杂问题。我们相信，这样一支计算生物学家队伍的发展将促进基础生物和生物医学科学前沿的发现。

DESCRIPTION (provided by applicant): The Tri-Institutional Training Program in Computational Biology and Medicine (CBM) takes advantage of the outstanding educational and research resources of Cornell University in Ithaca, NY, the Weill Cornell Medical College in New York City, and the Sloan-Kettering Institute (the research arm of the Memorial Sloan-Kettering Cancer Center) to train computational biologists in the interdisciplinary approaches they need to solve the complex problems that characterize biology and medicine. The CBM training environment is designed to address the unique challenges of training scientists in this area and is characterized by: coursework in both quantitative and biological sciences; research rotations to enable a well-informed thesis topic selection; trainee Research-in-Progress seminar series to enhance program cohesion and foster fluency in relevant disciplines; thesis research in one of a diverse array of basic to translational computational and experimental laboratories; both quantitative and biological mentorship to ensure training balance and breadth; and encouragement to engage in hybrid computational/experimental projects to foster connections between theory and experiment. A theme underlying all program components is trainee exposure to diverse approaches and areas of computational biology, a reflection of the fact that interdisciplinary approaches are invaluable for solving many problems in biology and medicine today. The program, which is well established in its 10th year, has an expanding record of training success. With this competitive renewal proposal, we are requesting an increase from 6 to 12 T32 slots, which we believe is justified by the deep pool of highly qualified training-grant eligible applicants, large array of cutting-edge thesis research opportunities with leading faculty scientists, and the training enrichment that is inherent to an increased critical mass of students. The requested T32 funding would greatly aid the CBM program in continuing to achieve its mission of training the next generation of scientists to use computational and analytical methods to solve complex problems in biology. It is our belief that the development of such a cadre of computational biologists will foster discovery in frontiers of basic biological and biomedical sciences.